mdeberta-v3-base-finetuned-climate-support-new

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5523
• Accuracy: 0.8990
• Accuracy Balanced: 0.8461
• Precision Macro: 0.8550
• Recall Macro: 0.8461
• F1 Macro: 0.8504
• Precision Micro: 0.8990
• Recall Micro: 0.8990
• F1 Micro: 0.8990
• Precision Weighted: 0.8978
• Recall Weighted: 0.8990
• F1 Weighted: 0.8983
• Precision Class 0: 0.7788
• Recall Class 0: 0.7521
• F1 Class 0: 0.7652
• Support Class 0: 468
• Precision Class 1: 0.9312
• Recall Class 1: 0.9401
• F1 Class 1: 0.9356
• Support Class 1: 1670

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.06
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Accuracy Balanced	Precision Macro	Recall Macro	F1 Macro	Precision Micro	Recall Micro	F1 Micro	Precision Weighted	Recall Weighted	F1 Weighted	Precision Class 0	Recall Class 0	F1 Class 0	Support Class 0	Precision Class 1	Recall Class 1	F1 Class 1	Support Class 1
0.4698	0.6046	500	0.4537	0.8770	0.7744	0.8435	0.7744	0.8010	0.8770	0.8770	0.8770	0.8714	0.8770	0.8701	0.7937	0.5919	0.6781	468	0.8932	0.9569	0.9240	1670
0.3089	1.2092	1000	0.5018	0.8873	0.7825	0.8692	0.7825	0.8145	0.8873	0.8873	0.8873	0.8839	0.8873	0.8798	0.8429	0.5962	0.6984	468	0.8954	0.9689	0.9307	1670
0.2397	1.8138	1500	0.4276	0.8896	0.8178	0.8480	0.8178	0.8314	0.8896	0.8896	0.8896	0.8862	0.8896	0.8871	0.7802	0.6902	0.7324	468	0.9159	0.9455	0.9305	1670
0.1779	2.4184	2000	0.5046	0.8648	0.8543	0.7991	0.8543	0.8200	0.8648	0.8648	0.8648	0.8839	0.8648	0.8705	0.6484	0.8355	0.7302	468	0.9498	0.8731	0.9098	1670
0.1417	3.0230	2500	0.5791	0.8784	0.8506	0.8168	0.8506	0.8315	0.8784	0.8784	0.8784	0.8870	0.8784	0.8815	0.6919	0.8013	0.7426	468	0.9417	0.9	0.9204	1670
0.091	3.6276	3000	0.5523	0.8990	0.8461	0.8550	0.8461	0.8504	0.8990	0.8990	0.8990	0.8978	0.8990	0.8983	0.7788	0.7521	0.7652	468	0.9312	0.9401	0.9356	1670

Framework versions

• Transformers 4.56.2
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1